Publication rates of research presented at the Canadian Society of Otolaryngology-Head and Neck Surgery Annual Meetings from 2008 to 2018: An 11-year review
Bibliographic record
Abstract
BACKGROUND: Knowledge dissemination is paramount so physicians may practice the most up-to-date, evidence-based medicine to best serve their patients. Medical conferences are a commonly employed method of facilitating this. By determining the publication rate of research presented at a conference, the quality of the conference is indirectly assessed. Therefore, this study aimed to determine the publication rate, along with other conference metrics, of abstracts presented at the Canadian Society of Otolaryngology-Head and Neck Surgery (CSOHNS) meetings from 2008 to 2018. METHODS: All abstracts presented at the CSOHNS Annual Meetings from 2008 to 2018 were reviewed from publicly available records. Presentation year, presentation type (i.e. oral or poster), whether each abstract was presented in the Poliquin Resident Research Competition, and the country in which the lead author's institution was located, were collected. Publication status of each abstract was then determined using a six-tiered search strategy in PubMed and Google Scholar. All data were then analyzed using SPSS Version 27.0. RESULTS: From 2008 to 2018, 1947 abstracts were analyzed, yielding an overall publication rate of 58.7%. There was a significantly increasing trend in publication rate over the 11 years (p = 0.015). The rate of publication differed based on type of presentation (oral 65.1%, poster 50.2%; p = 0.001). Most presentations were presented by a first author associated with a Canadian institution (94.4%). The top journal in which research was published was Journal of Otolaryngology- Head and Neck Surgery (37.3%). The mean impact factor of the journals in which presentations were published was 2.92. Finally, the median time to publication was 14 months (IQR: 9.0-25.0). CONCLUSIONS: Research presented at 2008-2018 CSOHNS annual meetings was published in academic journals at higher rates than research at comparable conferences. Oral presentations have a significantly greater publication rate, compared to poster presentations. Additionally, the upward trend in publication rate over the 11 meetings suggests a steady increase in the quality of research being presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.037 | 0.043 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".